CN114735165A - Intelligent underwater lifesaving system and drowning detection and rescue method - Google Patents
Intelligent underwater lifesaving system and drowning detection and rescue method Download PDFInfo
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Abstract
The invention relates to an intelligent underwater lifesaving system which comprises an underwater robot, an unmanned aerial vehicle, a server and an intelligent bracelet. The underwater robot comprises a main body, a human body supporting seat, a life belt and life fish, wherein a first turbine for providing ascending and descending power in water is arranged on the main body, and a second turbine for providing advancing, retreating and steering power in water is arranged at the tail of the main body. The intelligent bracelet comprises a second microprocessor, and a heart rate sensor, a water pressure sensor, a third GPS module, a fourth wireless communication module and a fourth geomagnetic sensor which are respectively connected with the second microprocessor. The invention provides an underwater robot for underwater rescue, which is characterized in that a life belt is used for winding drowned people to lift the head of a person upwards out of the water surface, and compared with manual rescue, the underwater robot has high rescue efficiency and good practicability; the invention realizes the integration of an underwater robot, an unmanned aerial vehicle, an intelligent bracelet and a server, and the server carries out real-time drowning detection by utilizing images, physiological data provided by the intelligent bracelet and the like, thereby finding the drowning phenomenon as early as possible and carrying out rescue in time.
Description
Technical Field
The invention belongs to the field of water lifesaving, and particularly relates to an intelligent underwater lifesaving system and a drowning detection and rescue method.
Background
The water rescue has very high requirements on timeliness and stability, and relates to a plurality of leading-edge technologies in a wide subject field such as computers, automatic control, sensors and the like.
From the current research situation at home and abroad of water rescue, the research results of the maneuvering rescue of the unmanned aerial vehicle and the underwater robot are relatively few, most of the maneuvering rescue is still in the stage of primary research and test, and the comprehensive technical level and the service level are low. Aiming at swimming scenes of different water areas with dense people and sparse people, the overwater rescue strategy in the prior art only realizes how to detect and position, wherein how to realize quick maneuvering rescue, the quick rescue service providing is not well realized, and the phenomenon that the rescue service cannot be provided in time because people know to drown exists; meanwhile, the application of the rescue technology compatible with open water areas and indoor swimming venues and the practicability of rescue are lacked. The intelligent bracelet and the rescue unmanned aerial vehicle are not fused, so that the existing domestic and foreign water life-saving system is always incomplete, a large gap exists in practical application, and the comprehensive technical level and the service level of the rescue scheme are low.
Disclosure of Invention
The invention aims to solve the problems and provides an intelligent underwater lifesaving system and a drowning person rescue method, wherein an underwater robot for performing underwater autonomous rescue on drowning persons is designed and provided, the underwater robot, an unmanned aerial vehicle, an intelligent bracelet and a server are integrated, and the underwater robot and the intelligent bracelet are used for performing indoor and outdoor accurate positioning by utilizing the complementation of a geomagnetic positioning technology and a GPS positioning technology; the image of gathering is shot in real time according to underwater robot or the camera under water to the server on the one hand, utilize the gesture of the human body in the neural network model discernment image, judge whether there is drowned phenomenon according to human gesture, on the other hand is according to the aquatic personnel's of intelligent bracelet real-time collection rhythm of the heart, degree of depth and X, Y, Z axle acceleration under water, judge whether drowned phenomenon of personnel appears through machine learning algorithm real-time, detect drowned phenomenon through two kinds of modes real-time, discover drowned personnel as early as possible, avoid the rescue untimely lead to the casualties.
The invention has the technical scheme that the intelligent underwater lifesaving system comprises an underwater robot, wherein the underwater robot comprises a main body, a human body supporting seat, a lifesaving belt and lifesaving fish, and the front end of the lifesaving belt is connected with the lifesaving fish; the main body is internally provided with a controller, a first wireless communication module and a first GPS module which are connected with the controller, the head part of the main body is provided with a camera, and the camera is connected with the controller through a data line; the main body is provided with a first turbine for providing ascending and descending power in water, the tail part of the main body is provided with a second turbine for providing advancing, backing and steering power in water, and the control ends of the first turbine and the second turbine are respectively connected with the controller; the tail end of the life belt is fixedly connected with the side part of the main body.
The lifesaving fish comprises a fish-imitating body, a bionic fin and a propeller thruster arranged at the tail part of the fish-imitating body, wherein the rear end of the propeller thruster is provided with a traction part for connecting and dragging the lifesaving belt; the fish-imitating body is internally provided with a first microprocessor, a second wireless communication module and a second geomagnetic sensor which are respectively electrically connected with the first microprocessor, and the control end of a driving motor of the propeller thruster is connected with the control signal output end of the first microprocessor.
Furthermore, the longitudinal axis of the bionic fin is connected with the output shaft of the steering engine, and the control end of the steering engine is connected with the control signal output end of the first microprocessor. Under the driving of the steering engine, the advancing direction of the life-saving fish is controlled by adjusting the angle between the bionic fin and the central line of the propeller.
Preferably, the underwater robot further comprises a first geomagnetic sensor, and the first geomagnetic sensor is connected with the controller through a data line.
Preferably, the system further comprises an unmanned aerial vehicle, a connecting rod used for being connected with the underwater robot is arranged below a cantilever of the unmanned aerial vehicle, an electric hydraulic clamp is arranged at the connecting end of the connecting rod and the underwater robot, the electric hydraulic clamp is matched with a connecting hole at the top of the underwater robot, and the control end of the electric hydraulic clamp is connected with a control unit of the unmanned aerial vehicle; the unmanned aerial vehicle also comprises a second GPS module and a second wireless communication module, and the second GPS module and the second wireless communication module are respectively connected with the control unit of the unmanned aerial vehicle.
Preferably, the system further comprises an intelligent bracelet, wherein the intelligent bracelet comprises a single chip microcomputer and a heart rate sensor, a water pressure sensor, a third GPS module, a third wireless communication module and a second geomagnetic sensor which are connected with the single chip microcomputer respectively.
Preferably, the system further comprises a server, the server is in communication connection with the underwater robot, the unmanned aerial vehicle and the smart bracelet respectively, and the server detects whether the human body in the image drowns according to the images provided by the underwater robot and the unmanned aerial vehicle; the server carries out drowning detection according to the heart rate, the underwater depth and the triaxial acceleration data of the person collected by the intelligent bracelet.
Furthermore, the underwater robot also comprises a target recognition module which runs on a processor of the controller, the target recognition module recognizes a human body in water according to an image shot by the camera, and judges whether the human body is a rescue target according to the position and the image characteristics of the human body.
A human body drowning detection method based on an underwater human body image comprises the following steps:
step 1: shooting to obtain an image of a human body in water;
and 2, step: carrying out human body target detection on the image by using a YOLOv5s model to obtain a human body positioning frame;
and step 3: recognizing the human body posture in the human body positioning frame by adopting an Alphapos model to obtain a bone key point and determine a coordinate of the bone key point;
and 4, step 4: calculating to obtain the body posture characteristics of the human body according to the coordinates of the key points of the human skeleton;
and 5: judging whether drowning occurs according to the body state characteristics of the human body, judging the length of drowning time if drowning occurs, and giving an alarm. A human body drowning detection method based on an intelligent bracelet comprises the following steps:
s1: the heart rate, the underwater depth and the X, Y, Z-axis acceleration of normal swimming and simulated drowning persons in water are obtained by using the intelligent wristband respectively;
s2: constructing a drowning detection model by using a support vector machine, and training the drowning detection model by using the data obtained in the step S1 as the input of the drowning detection model;
s3: acquiring the heart rate, the underwater depth and the X, Y, Z axis acceleration of a human body to be detected in water in real time by using an intelligent bracelet;
s4: taking the data acquired in the step S3 as input of a drowning detection model, performing real-time drowning detection, if the drowning is detected, executing the step S5, otherwise executing the step S3;
s5: and acquiring the position of the drowned human body and sending out a drowning alarm.
The drowning person rescue method comprises the following steps:
1) detecting drowning of a human body in water, determining the position of a drowning person when the drowning phenomenon of the human body is detected, and executing the step 2), otherwise, repeatedly executing the step 1);
2) throwing an underwater robot at a rescue place according to the position of drowned people;
3) controlling the underwater robot to travel to the position of drowned people in water, collecting images of the people at the position, and judging and confirming whether the underwater robot is a rescue target;
4) controlling the underwater robot to enable a human body supporting seat of the underwater robot to be aligned with the back of a drowning person;
5) controlling the rescue fish to draw the rescue belt to surround the body of the drowned person; after the life-saving fish pulls the life-saving belt to surround the drowned human body, the life-saving fish is controlled to perform circular motion around the human body supporting seat, and the life-saving belt is continuously tightly wound at the root of the human body supporting seat; after the life belt is wound, the life-saving fish is controlled to swim back to the containing chamber, and the life belt is inflated;
6) and controlling the underwater robot to drive to the water surface to enable the head of the drowning person to be exposed out of the water surface.
Compared with the prior art, the invention has the beneficial effects that:
1) the invention provides an underwater robot for performing underwater rescue on drowned people, which is provided with a human body supporting seat and a life belt, can be automatically lifted and turned in water, and when the drowned people are rescued, the drowned people are wound on the human body supporting seat by the life belt to lift the head of the human body out of the water surface, so that compared with manual rescue, the underwater robot has high rescue efficiency and good practicability, and can avoid unnecessary damage to the drowned human body during rescue;
2) the underwater robot comprises the lifesaving fish, when the drowned person is rescued, the lifesaving fish pulls the lifesaving belt to complete the surrounding of the body of the drowned person, the independence of the lifesaving fish is good, the head of the drowned person can be conveniently lifted away from the water surface after the body of the drowned person is wound on the body supporting seat of the underwater robot, and the drowned person can be rescued in time;
3) the underwater robot, the unmanned aerial vehicle, the intelligent bracelet and the server are integrated, on one hand, the server shoots acquired images in real time according to the underwater robot, recognizes the posture of a human body in the images and judges whether drowning occurs or not, on the other hand, the server judges whether drowning occurs or not in real time according to the heart rate, the underwater depth and the X, Y, Z axial acceleration of underwater personnel acquired by the intelligent bracelet in real time, the drowning phenomenon is detected in real time through two modes, the drowning personnel are found in time, and the efficiency of drowning inspection and personnel rescue is improved; the server is convenient for the underwater robot to quickly reach the position of drowning personnel for rescue through the positioning of the intelligent bracelet of the drowning personnel; after the unmanned aerial vehicle flies to the position of drowned personnel, the underwater robot is thrown in the air, and the rescue efficiency is further improved;
4) the invention utilizes the geomagnetic positioning technology to make up the defect that the indoor GPS positioning signal is weak, realizes accurate positioning of the drowning person in indoor water, and is convenient for timely rescue;
5) according to the method, a YOLOv5s model is used for detecting the human body target of the image, after a human body positioning frame is obtained, an improved Alphapos model is used for identifying the human body posture in the human body positioning frame, skeleton key points are obtained, coordinates of the skeleton key points are determined, human body posture characteristics are obtained through calculation, the real-time judgment of drowning phenomenon of personnel is realized, and the drowning detection accuracy is high;
6) according to the intelligent hand ring drowning detection method, a support vector machine is utilized, whether the drowning phenomenon of the person occurs or not is judged in real time according to the heart rate, the underwater depth and the X, Y, Z axis acceleration of the person in water acquired by the intelligent hand ring, the drowning detection real-time performance is good, and the accuracy rate is high;
7) the rescue method has high rescue success rate and good protection for drowned people in the rescue process.
Drawings
The invention is further illustrated by the following figures and examples.
Fig. 1 is a schematic structural diagram of an intelligent underwater lifesaving system in an embodiment of the invention.
Fig. 2 is a schematic view of an underwater robot according to an embodiment of the present invention.
Fig. 3 is a schematic view of a human body support base of the underwater robot according to the embodiment of the present invention.
Fig. 4 is a schematic structural view of a life-saving fish according to an embodiment of the present invention.
Fig. 5 is a schematic view of an unmanned aerial vehicle according to an embodiment of the present invention.
Fig. 6 is a schematic structural diagram of the YOLOv5s model.
Fig. 7 is a schematic diagram of an improved YOLOv5s model according to an embodiment of the present invention.
FIG. 8 is a schematic diagram of the Alphapos model according to an embodiment of the invention.
FIG. 9a is a schematic diagram of key points of human bones according to an embodiment of the present invention.
FIG. 9b is a schematic diagram of the underwater human bone key points according to the embodiment of the present invention.
Fig. 10 is a schematic flow chart of a human drowning detection method according to an embodiment of the present invention.
Detailed Description
As shown in fig. 1, the intelligent underwater lifesaving system of the embodiment includes a server, and an underwater robot 100, an unmanned aerial vehicle 200, an intelligent bracelet 300 and a monitoring terminal which are in communication connection with the server respectively. The server detects whether the human body in the image is drowned according to the images provided by the underwater robot 100 and the unmanned aerial vehicle 200.
As shown in fig. 2 to 4, the underwater robot 100 includes a main body 101, a human body support base 105, a life-saving belt 103 and life-saving fish 102, wherein the front end of the life-saving belt 103 is connected with the life-saving fish 102; a controller, a first wireless communication module, a first GPS module and a first geomagnetic sensor which are connected with the controller are arranged in the main body 101, a camera 104 is arranged at the head of the main body, and the camera 104 is connected with the controller through a data line; the main body 101 is provided with a first turbine 106 for providing ascending and descending power in water, the tail part of the main body 101 is provided with a second turbine 107 for providing advancing, backing and steering power in water, and the control ends of the first turbine 106 and the second turbine 107 are respectively connected with a controller. The two sides of the human body supporting seat 105 are respectively provided with a groove 111 which is convenient for winding the life belt.
As shown in fig. 3, a high-pressure gas tank 108 for inflating the survival belt 103 is provided on the main body 101 side of the underwater robot 100. The other side of the main body 101 is provided with a receiving chamber 109 for receiving life belts and life fish. The life belt 103 is a hollow airtight belt-shaped structure, the inflation end of the life belt 103 is connected with the high-pressure air tank 108 through an electromagnetic valve, and the control end of the electromagnetic valve is connected with the control signal output end of the controller. In the embodiment, the high-pressure gas tank 108 stores liquid nitrogen gas therein.
As shown in fig. 4, the life-saving fish 102 comprises a fish-imitating body 1021, a bionic fin 1024 and a propeller 1022 arranged at the tail of the fish-imitating body, wherein the rear end of the propeller 1022 is provided with a traction part 1023 for connecting and dragging a life-saving belt; a first microprocessor, a second wireless communication module and a second geomagnetic sensor which are electrically connected with the first microprocessor are arranged in the fish-imitating body 1021, and a control end of a driving motor of the propeller 1022 is connected with a control signal output end of the first microprocessor.
The longitudinal axis of the bionic fin 1024 is connected with the output shaft of the steering engine, and the control end of the steering engine is connected with the control signal output end of the first microprocessor. Under the driving of the steering engine, the advancing direction of the life-saving fish 102 is controlled by adjusting the angle between the bionic fin 1024 and the central line of the propeller 1022.
The unmanned aerial vehicle, underwater robot, intelligent bracelet of embodiment all adopt the mode that earth induction location and GPS location combined together, utilize geomagnetic sensor to carry out the accurate positioning in the environment that GPS signals are weak such as indoor swimming pool.
The smart band 300 comprises a second microprocessor, a heart rate sensor, a water pressure sensor, a third GPS module, a fourth wireless communication module and a third geomagnetic sensor which are respectively connected with the second microprocessor, and the smart band 300 is in communication connection with a server. The smart band 300 obtains the underwater depth of the smart band using the real-time data output by the water pressure sensor.
As shown in fig. 5, a connecting rod 201 for connecting with the underwater robot is arranged below a cantilever of the unmanned aerial vehicle 200, an electric hydraulic clamp 202 is arranged at a connecting end of the connecting rod 201 and the underwater robot, a bite head of the electric hydraulic clamp 202 is matched with a connecting hole 111 at the top of the underwater robot, and a control end of the electric hydraulic clamp 202 is connected with a control unit of the unmanned aerial vehicle; the unmanned aerial vehicle 200 further comprises a second GPS module and a third wireless communication module, and the second GPS module and the third wireless communication module are respectively connected with the control unit of the unmanned aerial vehicle.
In the embodiment, when the unmanned aerial vehicle 200 transports the underwater robot 100 in the air, the control unit respectively controls the engagement heads of the electric hydraulic tongs 202 at the bottom ends of the connecting rods below the 4 cantilevers of the unmanned aerial vehicle to engage with the connecting hole 111 at the top of the underwater robot, so that the underwater robot 100 is connected with the unmanned aerial vehicle 200, and the unmanned aerial vehicle 200 carries the underwater robot 100 to fly. When the unmanned aerial vehicle 200 puts in the underwater robot 100 in the air, the control unit respectively controls the bite heads of the electric hydraulic tongs 202 at the bottom ends of the connecting rods below the 4 cantilevers of the unmanned aerial vehicle to open, and the underwater robot 100 is separated from the unmanned aerial vehicle 200 under the action of gravity to complete operation.
The second wireless communication module of the life-saving fish 102 is in communication connection with the first wireless communication module of the underwater robot 100; the first wireless communication module of the underwater robot 100, the third wireless communication module of the unmanned aerial vehicle 200, and the fourth wireless communication module of the smart band 300 are in communication connection with the server through wireless routers, respectively.
The postures of a person in water can be roughly classified into two types: one is parallel to the water surface, one is perpendicular to the water surface, and the other can be categorized as perpendicular to the water surface. Generally, people drown can be classified into a second type, the posture is vertical to the water surface, at the moment, people's consciousness still exists and can still last for a longer time, and the drowning characteristic of the people is easier to obtain. Therefore, when the person in water just shows drowning, the reliable and rapid detection of the drowning state has more practical significance. As shown in fig. 9a, 18 skeletal key points of the human body are extracted, and the posture of the human body is determined by the relative positions of the 18 skeletal key points.
When drowning, the posture characteristic of human body includes:
1) drowned characteristic under water: the feet swing ceaselessly, the body floats up and down, and the position coordinates of the feet change along with the time;
2) final drowning characteristics: the human body is parallel to the water surface and floats on the water surface, and whether the key skeleton points change relatively or not can be judged along with the time so as to draw a conclusion whether the final drowning characteristic appears or not.
When a person is just drowned, the body of the person often faces upwards, the legs of the person under water float up and down, the whole body of the person is not easily recognized from an image shot under the water surface, and the change of the relative position of the bone key points with time can be detected by taking the bone key points of the lower half of the person shown in fig. 9b as a core, so that the state of drowning under water can be detected.
As shown in fig. 10, the method for detecting human body drowning based on the human body image in water includes the following steps:
step 1: shooting to obtain an image of a human body in water;
step 2: carrying out human body target detection on the image by utilizing an improved YOLOv5s model to obtain a human body positioning frame;
as shown in fig. 6, the network model of YOLOv5s includes a Backbone network backhaul, a multi-scale feature fusion module Neck, and a Prediction end Prediction.
In the first layer Focus of the backhaul network, YOLOv5s of the embodiment reconstructs a high-resolution image, stacks four points around each pixel point to obtain a low-resolution image, improves the field of view of each point, and reduces loss of original information, so as to reduce the amount of computation and increase the computation speed. The third layer C3/BottleNeckCSP of the Backbone network comprises a Bottleneck part and a CSP part, and the aim is to improve the learning capability of the convolutional neural network. And after adopting 5/9/13 maximum pooling, the SPP module of the ninth layer of the Backbone network performs concat fusion, and improves the visual field of each point. The multi-scale feature fusion module Neck of YOLOv5s of the embodiment adopts Mask R-CNN and FPN frameworks to help pixels convey strong positioning features, and the network feature fusion capability can be enhanced by using the Mask R-CNN and the FPN frameworks simultaneously.
The Prediction module of yollov 5s of an embodiment contains a loss function Bounding box and a non-maximum Suppression (NMS). The parameter iou-thres is added into yolov5s in the embodiment, so that the problem of non-overlapping of the boundary frames is effectively solved, the value is set to be 0.45, the effect is best, and the regression speed and precision of the prediction frame are effectively improved. According to the embodiment, the YOLOv5s uses NMS in the human body detection positioning stage, the recognition capability of complex environments such as light shadow, coverage and the like is enhanced, and an optimal target detection frame is obtained.
As shown in fig. 7, in the modified YOLOv5s model of the embodiment, based on the YOLOv5s model, convolution weight coefficients are added to both the convolutional layer of the backhaul network and the convolutional layer of the head network, upsampling coefficients are added to both the upsampling layer of the backhaul network and the upsampling layer of the head network, the number of bottleeckcsp modules is set to (3, 6, 6, 3), and the number of categories nc is set to 1.
The improved object detection model YOLOv5s of the embodiment has a model parameter number of 8.1M, and the prior art YOLOv5s has a model parameter number of 7.1M, where M represents mega.
And 3, step 3: recognizing the human body posture in the human body positioning frame by adopting an Alphapos model to obtain a skeleton key point and determine the coordinate of the skeleton key point;
as shown in fig. 8, the alphaphase model of the embodiment uses a Regional Multi-Person position Estimation Network (RMPE) to detect and recognize the Multi-Person position according to the human body positioning frame of the image, and the RMPE includes a Space Transformation Network (STN), a Single Person Position Estimation (SPPE), a Spatial De-Transformer Network (SDTN), and a position Non-Maximum suppressor (PPNMS) connected in sequence.
The STN is used for segmenting and extracting a single human body positioning frame from the images of the plurality of human body positioning frames so as to further perform single posture recognition; the SDTN is used for converting and restoring the detected and recognized human posture into an original image, the SDTN realizes a reverse process of the STN, and the STN and the SDTN form a Symmetric Space Transformation Network (SSTN). The SPPE is used for detecting and identifying human postures according to the human body positioning frame. The SSTN and SPPE combination of the embodiment realizes the detection and identification of the postures of a plurality of persons in the image. The PPNMS is used for eliminating redundant attitude estimation, and deleting redundant detection frames by defining the distance calculation similarity degree of the attitude, so that the accuracy of human attitude detection and identification is improved.
In the process of training RMPE, a Pipeline parallel to SSTN is added, and the parallel Pipeline comprises a position-Guided probes Generator (PGPG) and a parallel SPPE (parallel SPPE). The PGPG generates samples of the human body positioning frames which obey specific distribution according to the determined real postures and the human body positioning frames in the training samples, and increases the inaccurate samples of the human body positioning frames so as to train and improve the adaptability of the SSTN and SPPE combined posture detection model of the embodiment to the inaccurate human body positioning frames. In order to improve the accuracy of human posture detection and recognition, the posture mark output by the SPPE is centered. The parallel SPPE and the SPPE combined with the SSTN in the posture detection model share the STN, the posture mark of the parallel SPPE is compared with the real centered human posture of the training sample in the training process, the deviation is calculated, the deviation is reversely transmitted to the STN so as to train the STN, the STN can be focused in a correct area, and a high-accuracy human body area is extracted.
RMPE of the examples refer to the paper "RMPE: RMPE model disclosed by Regional Multi-Person Pose Estimation.
In the embodiment, the human body target in the image is detected by using the alphaphase model, so that 18 key points of the human body skeleton shown in fig. 9a are obtained, and the coordinates of the key points in the image are obtained.
And 4, step 4: calculating to obtain human body posture characteristics according to the coordinates of key points of human skeleton, judging whether drowning occurs according to the human body posture characteristics, judging the length of drowning time if drowning occurs, and giving an alarm;
step 4.1: respectively calculating the linear velocity V of the key points (10) and (13) of bones corresponding to the feet of the human body10、V13And the linear velocity of the key points (11) and (13) of the skeleton is used for calculating the velocity V of the single leg of the human body11-13(ii) a Calculating linear velocity V of human chest gravity center OO；
In an embodiment, the linear velocity is calculated from image frames with time intervals in consecutive M-frame images. The position of the center of gravity O of the chest of the human body is calculated according to the coordinates of the key points (1), (8) and (11) of the bones.
And 4.2: calculating the linear velocity V of the human foot10、V13Linear velocity V relative to human chest center of gravity OOThe proportion of (2) preliminarily judging whether the drowning phenomenon occurs or not;
the formula for judging drowning is as follows:
in the formula, lambda represents a set threshold value, and alpha and beta respectively represent the upper limit and the lower limit of the average linear velocity;
if V10、V13、VO、V11-13If the formula (I) is satisfied, primarily judging that the underwater drowning occurs, and ending; if V10、V13、VO、V11-13If the formula (I) is not satisfied, executing the step 4.3;
step 4.3: calculating the included angle theta between the perpendicular bisector of the upper half of the human body and the horizontal planeaIf theta0＜θa＜θ1Angle of inclination thetaaIs continuously kept in the range for a time T, and a < VOIf < b, the drowning is judged to be finally done, and the operation is finished.
In the formula [ theta ]0Represents the minimum value of the angle between the perpendicular bisector of the upper half of the drowned human body and the horizontal plane, theta1Indicating the middle vertical line and the horizontal plane of the upper half of the drowned human bodyThe maximum value of the included angle, a represents the lower threshold value of the linear speed, and b represents the upper threshold value of the linear speed; otherwise, executing step 4.4;
step 4.4: calculating the included angle theta between the perpendicular bisector and the horizontal line of the human bodybIf theta2＜θb＜θ3Angle of inclination thetabIs continuously kept in the range for a time T, and c < VoD, and the key points of the bones of the human head can be detected, the drowning is judged to be finally done, otherwise, the drowning is judged not to be done, and theta is shown in the formula2The minimum value of the angle between the perpendicular bisector and the horizontal plane, theta, of a drowned person3The included angle between the perpendicular bisector and the horizontal plane of the drowned human body is represented as the maximum value, c represents the minimum value of the linear velocity, and d represents the maximum value of the linear velocity.
A human body drowning detection method based on an intelligent bracelet comprises the following steps:
s1: the heart rate, the underwater depth and the X, Y, Z-axis acceleration of normal swimming and simulated drowning persons in water are obtained by using the intelligent wristband respectively;
s2: constructing a drowning detection model by using a support vector machine, and training the drowning detection model by using the data obtained in the step S1 as the input of the drowning detection model;
s3: acquiring the heart rate, the underwater depth and the X, Y, Z axis acceleration of a human body to be detected in water in real time by using an intelligent bracelet;
s4: taking the data acquired in the step S3 as input of a drowning detection model, performing real-time drowning detection, if the drowning is detected, executing a step S5, otherwise executing a step S3;
s5: and acquiring the position of the drowned human body and sending out a drowning alarm.
The support vector machine in step S2 refers to the support vector machine model disclosed in "review of theory and algorithm research on support vector machine" thesis of support vector machine ", published by dingshifei et al, journal of university of electronic technology, volume 40, 2011.
The drowning person rescue method comprises the following steps:
1) the method comprises the steps that a server carries out drowning detection according to images of human bodies in water collected by an unmanned aerial vehicle, an underwater camera or an underwater robot and the like, or carries out drowning detection according to heart rate, underwater depth and triaxial acceleration data of people collected by an intelligent bracelet, when the drowning phenomenon of the human body is detected, the position of a drowning person is determined, the positioning coordinate of the drowning person and the image of the drowning person are sent to the underwater robot, the positioning coordinate of the drowning person is sent to the unmanned aerial vehicle, step 2) is executed, and otherwise, step 1) is repeatedly executed;
2) the unmanned aerial vehicle puts in an underwater robot at a rescue place according to the positioning coordinates of drowned people;
3) controlling the underwater robot to travel to the position of drowned people in water, collecting images of the people at the position, and judging and confirming whether the underwater robot is a rescue target;
4) controlling the underwater robot to enable a human body supporting seat of the underwater robot to be aligned with the back of a drowning person;
5) controlling the rescue fish to draw the rescue belt to surround the body of the drowned person; after the life-saving fish pulls the life-saving belt to surround the drowned human body, the life-saving fish is controlled to perform circular motion around the human body supporting seat, and the life-saving belt is continuously tightly wound at the root of the human body supporting seat; after the life belt is wound, the life-saving fish is controlled to swim back to the containing chamber, and the life belt is inflated;
6) and controlling the underwater robot to drive to the water surface to enable the head of the drowning person to be exposed out of the water surface.
The improved YOLOv5s model is a lightweight model, balance between detection precision and speed is achieved, and timeliness of detection of drowning phenomena of people is improved.
When the underwater robot 100 inflates the life belt, the electromagnetic valve at the inflation end of the life belt 103 is opened through the controller, and liquid nitrogen in the high-pressure gas tank 108 is gasified after flowing through the electromagnetic valve, so that the life belt 103 is filled.
In the embodiment, the underwater robot 1 further includes a target recognition module running on a processor of the controller, and the target recognition module recognizes a human body in water according to an image shot by the camera 104, compares the image feature of the human body with an image feature of drowned people provided by the server according to the image feature of the human body, and further determines whether the image feature is a rescue target.
Claims (10)
1. The intelligent underwater lifesaving system is characterized by comprising an underwater robot (100), wherein the underwater robot (100) comprises a main body (101), a human body supporting seat (105), a lifesaving belt (103) and lifesaving fish (102), and the front end of the lifesaving belt (103) is connected with the lifesaving fish (102);
a controller, a first wireless communication module and a first GPS module which are connected with the controller are arranged in the main body (101), a camera (104) is arranged at the head of the main body, and the camera (104) is connected with the controller through a data line;
the main body (101) is provided with a first turbine (106) for providing ascending and descending power in water, the tail part of the main body (101) is provided with a second turbine (107) for providing advancing, backing and steering power in water, and the control ends of the first turbine (106) and the second turbine (107) are respectively connected with the controller;
the end of the life belt (103) is connected to the side of the body (101).
2. An intelligent underwater rescue system according to claim 1, characterized in that the underwater robot (100) further comprises a first geomagnetic sensor connected to a controller thereof.
3. The intelligent underwater lifesaving system according to claim 2, wherein the lifesaving fish (102) comprises a fish-imitating body (1021), a bionic fin (1024) and a propeller thruster (1022) arranged at the tail of the fish-imitating body, and a traction part (1023) for connecting and dragging the lifesaving belt is arranged at the rear end of the propeller thruster (1022);
a first microprocessor and a second wireless communication module connected with the first microprocessor are arranged in the fish-imitating body (1021), and the control end of a driving motor of the propeller thruster (1022) is connected with the control signal output end of the first microprocessor;
the longitudinal axis of the bionic fin 1024 is connected with an output shaft of the steering engine, and the control end of the steering engine is connected with the control signal output end of the first microprocessor.
4. An intelligent underwater lifesaving system as claimed in claim 3, wherein the life belt (103) is a hollow airtight belt-shaped structure, the inflation end of the life belt (103) is connected with the high-pressure gas tank (108) through an electromagnetic valve, and the control end of the electromagnetic valve is connected with the control signal output end of the controller.
5. The intelligent underwater lifesaving system according to claim 2 or 3, characterized in that the system further comprises an unmanned aerial vehicle (200), a connecting rod (201) for connecting with the underwater robot is arranged below a cantilever of the unmanned aerial vehicle (200), an electric hydraulic clamp (202) is arranged at the connecting end of the connecting rod (201) and the underwater robot, the occlusion head of the electric hydraulic clamp (202) is matched with a connecting hole site (111) at the top of the underwater robot, and the control end of the electric hydraulic clamp (202) is connected with a control unit of the unmanned aerial vehicle;
the unmanned aerial vehicle (200) further comprises a second GPS module and a third wireless communication module, and the second GPS module and the third wireless communication module are respectively connected with the control unit of the unmanned aerial vehicle.
6. The intelligent underwater lifesaving system according to claim 5, wherein the system further comprises a server, the server is respectively in communication connection with the underwater robot (100) and the unmanned aerial vehicle (200), and the server detects whether the human body in the image is drowned according to the images provided by the underwater robot (100) and the unmanned aerial vehicle (200).
7. An intelligent underwater lifesaving system as claimed in claim 6, wherein the system further comprises an intelligent bracelet (300), the intelligent bracelet (300) comprises a second microprocessor and a heart rate sensor, a water pressure sensor, a third GPS module, a fourth wireless communication module and a third geomagnetic sensor which are respectively connected with the second microprocessor, and the intelligent bracelet (300) is in communication connection with the server.
8. The drowning person rescue method of the intelligent underwater rescue system of claim 6 or 7, comprising the steps of:
1) detecting drowning of a human body in water, determining the position of a drowning person when the drowning phenomenon of the human body is detected, and executing the step 2), otherwise, repeatedly executing the step 1);
2) throwing an underwater robot at a rescue place according to the position of drowned people;
3) controlling the underwater robot to travel to the position of drowned people in water, collecting images of the people at the position, and judging and confirming whether the underwater robot is a rescue target;
4) controlling the underwater robot to enable a human body supporting seat of the underwater robot to be aligned with the back of a drowning person;
5) controlling the rescue fish to draw the rescue belt to surround the body of the drowned person;
6) and controlling the underwater robot to drive to the water surface to enable the head of the drowning person to be exposed out of the water surface.
9. A human body drowning detection method based on a human body image in water is characterized by comprising the following steps:
step 1: shooting to obtain an image of a human body in water;
step 2: carrying out human body target detection on the image by using a YOLOv5s model to obtain a human body positioning frame;
and 3, step 3: recognizing the human body posture in the human body positioning frame by adopting an Alphapos model to obtain a bone key point and determine a coordinate of the bone key point;
and 4, step 4: calculating to obtain the body posture characteristics of the human body according to the coordinates of the key points of the human skeleton;
and 5: judging whether drowning occurs according to the body state characteristics of the human body, judging the length of drowning time if drowning occurs, and giving an alarm.
10. A human body drowning detection method based on an intelligent bracelet is characterized by comprising the following steps:
s1: the heart rate, the underwater depth and the X, Y, Z-axis acceleration of normal swimming and simulated drowning persons in water are obtained by using the intelligent wristband respectively;
s2: constructing a drowning detection model by using a support vector machine, and training the drowning detection model by using the data obtained in the step S1 as the input of the drowning detection model;
s3: acquiring the heart rate, the underwater depth and the X, Y, Z axis acceleration of a human body to be detected in water in real time by using an intelligent bracelet;
s4: taking the data acquired in the step S3 as input of a drowning detection model, performing real-time drowning detection, if the drowning is detected, executing a step S5, otherwise executing a step S3;
s5: and acquiring the position of the drowned human body and sending out a drowning alarm.
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